{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 项目开始\n",
    "```\n",
    "一.提出问题 \n",
    " 通过观察数据，现提出以下问题：\n",
    " 1.年龄,性别，仓位和登船港口是否对存活率有影响，若果有影响，分别是多少？哪个的影响更大？\n",
    " 2.在所有乘客中，有兄弟姐妹的占多少，他们的存活率是多少\n",
    " 3.在所有的乘客中，有父母子女的占多少，他们的存活率是多少\n",
    " 4.在哪个港口登船的乘客最多，每个港口登船乘客的存活率是多少 \n",
    " \n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "二.数据采集和清洗\n",
    " pandas读取titanic-data.csv的数据\n",
    " \n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "#加载titanic-data.csv\n",
    "filename = 'titanic-data.csv'\n",
    "titanic_df = pd.read_csv(filename)\n",
    "#print(titanic_df.head())\n",
    "#print(type(titanic_df))\n",
    "#print(titanic_df['Age'])\n",
    "\n",
    "titanic_df.info()\n",
    "print('')\n",
    "#观察发现有的数据并不能用来计算对应的生存率，所以取出我们要使用的数据 形成新的数据集\n",
    "new_titanic_df = titanic_df[['Survived','Pclass','Age','Sex','SibSp','Parch','Embarked']]\n",
    "#new_titanic_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总人数为 891 人，其中有 342 人生还，有 549 人死亡\n"
     ]
    }
   ],
   "source": [
    "#1.先计算总的存活率\n",
    "total_number = len(new_titanic_df)\n",
    "total_survived_number = new_titanic_df['Survived'].sum()\n",
    "total_no_survived_number = total_number - total_survived_number\n",
    "print('总人数为 %d 人，其中有 %d 人生还，有 %d 人死亡' % (total_number,total_survived_number,total_no_survived_number))\n",
    "#print(total_survived_number)\n",
    "survived_rate = total_survived_number / total_number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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lW5LUVkJPmAKcBWxccRSpShtRFHAvq1lnlm9JUtsIPWEccDqwddVZpAawCXAWIUypOkg7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\nKudfEMLZwH8BH6KYvikN5l8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x8bf7cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn\n",
    "\n",
    "%matplotlib inline\n",
    "labels = ['Survived', 'No Survived']\n",
    "data = [total_survived_number,total_no_survived_number]\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "plt.bar(range(len(data)), data, tick_label=labels,color='gr')\n",
    "plt.title('Survived Number')\n",
    "\n",
    "plt.subplot(122)\n",
    "explode = [0.05,0];\n",
    "plt.pie(data,explode=explode,labels=labels,colors='gr',\n",
    "                                labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,\n",
    "                                startangle = 90,pctdistance = 0.6)\n",
    "plt.title('Survived Rate')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "从图中可以看出总人数中的生还率为 38.4%\n",
    "然后计算 年龄,性别，仓位，登录港口分别对应的生存率\n",
    " \n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YmE+2jf0ObHWvoYh4HLAGeGdmrqpnXxgRz69v7wVc3WYmSSpd2yOCo4Ax4JiIOKae9w7g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\n5uR3xAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x4c87e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "count    714.000000\n",
       "mean      29.699118\n",
       "std       14.526497\n",
       "min        0.420000\n",
       "25%       20.125000\n",
       "50%       28.000000\n",
       "75%       38.000000\n",
       "max       80.000000\n",
       "Name: Age, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##年龄对应的生存率\n",
    "#首先看下年龄的分布图,因为年龄中有缺失的值，首先对缺失的数据进行处理，我们采用删除空的年龄来计算\n",
    "age_titanic_df = new_titanic_df['Age'].dropna()\n",
    "#print(age_titanic_df.to_frame())\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Number')\n",
    "plt.hist(age_titanic_df)\n",
    "plt.show()\n",
    "age_titanic_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "因为我们的数据最大的年龄为80  根据世界年龄段划分0-17未成年 18-65岁为青年 66-79岁为中年人 80-99为老年人\n",
    "所以将数据划分为4段\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "group_age\n",
       "(0, 17]     0.539823\n",
       "(17, 65]    0.384486\n",
       "(65, 79]    0.000000\n",
       "(79, 99]    1.000000\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame_age_titanic_df = age_titanic_df.to_frame()\n",
    "bins = [0, 17, 65, 79,99]\n",
    "frame_age_titanic_df.loc[:,'group_age'] = pd.cut(age_titanic_df, bins)\n",
    "frame_age_titanic_df.loc[:,'Survived'] = new_titanic_df['Survived']\n",
    "#print(frame_age_titanic_df)\n",
    "#计算各个年龄的生存率\n",
    "frame_age_titanic_df.groupby('group_age')['Survived'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "根据计算结果得到80-99年龄段的生存率为1不符合实际情况，所以重新查看数据找出年龄大于79的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Age</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>80.0</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived  Pclass   Age   Sex  SibSp  Parch Embarked\n",
       "630         1       1  80.0  male      0      0        S"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_titanic_df[ new_titanic_df['Age'] > 79]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "发现只有一条数据在80-99的年龄段中，所以我们对我们的年龄段重新划分\n",
    "按我国年龄段来划分 0-14岁为儿童 15-29为青少年  30-59为中年 60以上为老年 重新进行计算\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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7RBZbzvrDnWLOubrU998BhgDPEX5oX2et/dg592RrG+zbt5j8/LyuyJoTysqy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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0dd9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "group_age\n",
       "(0, 14]      0.584416\n",
       "(14, 29]     0.361564\n",
       "(29, 59]     0.417763\n",
       "(59, 100]    0.269231\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = [0, 14, 29, 59,100]\n",
    "frame_age_titanic_df.loc[:,'group_age'] = pd.cut(age_titanic_df, bins)\n",
    "age_survived_rate = frame_age_titanic_df.groupby('group_age')['Survived'].mean()\n",
    "\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "labels = ['(0-14]', '(14-29]','(29-59]','(59-100]']\n",
    "colors = ['red','yellowgreen','lightskyblue','green']\n",
    "explode = [0.05,0,0,0];\n",
    "plt.pie(age_survived_rate,explode=explode,labels=labels,colors=colors,\n",
    "                                labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,\n",
    "                                startangle = 90,pctdistance = 0.6)\n",
    "plt.title('Age Of Survived Rate')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.bar(range(len(age_survived_rate)), age_survived_rate, tick_label=labels,color=colors)\n",
    "plt.title('Survived Number')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "age_survived_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#存活率\n",
    "def plotRate(feature, stacked=False):\n",
    "    # if there are more than one features\n",
    "    if type(feature) is list:        \n",
    "        survived_rate = (new_titanic_df.groupby(feature).mean())['Survived'].unstack()\n",
    "        survived_rate.plot(kind='bar', stacked=stacked)\n",
    "        plt.title('Rate of Survival by {} and {}'.format(feature[0], feature[1]))\n",
    "    # if there is only one feature\n",
    "    else:\n",
    "        survived_rate = (new_titanic_df.groupby(feature).mean())['Survived']\n",
    "        survived_rate.plot(kind='bar')\n",
    "        plt.title('Rate of Survival by {}'.format(feature))\n",
    "\n",
    "# call the function to make plots\n",
    "# plotRate(\"Sex\")\n",
    "# plotRate([\"Pclass\", 'Sex'], False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 性别Sex\n",
    "\n",
    "### 1.船上所有男女比例是多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 7 columns):\n",
      "Survived    891 non-null int64\n",
      "Pclass      891 non-null int64\n",
      "Age         714 non-null float64\n",
      "Sex         891 non-null object\n",
      "SibSp       891 non-null int64\n",
      "Parch       891 non-null int64\n",
      "Embarked    889 non-null object\n",
      "dtypes: float64(1), int64(4), object(2)\n",
      "memory usage: 48.8+ KB\n",
      "在 891 人中 共有男性 577 人 ，女性 314 人\n"
     ]
    }
   ],
   "source": [
    "#男女比例\n",
    "new_titanic_df.info()\n",
    "count_survived_by_sex = new_titanic_df.groupby('Sex').count()['Survived']\n",
    "female_count = count_survived_by_sex['female']\n",
    "male_count = count_survived_by_sex['male']\n",
    "print ('在 891 人中 共有男性 %d 人 ，女性 %d 人' %(male_count,female_count))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5h2K09ZrBNqg3q1sBXwFe265Q6niLapWGV1eTJI1Ip428PkhxwoI6Uw6cRzHa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\nJEkqDcurJEmSSsPyKkmSpNL4/2Qij9pKyz6mAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb181780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "labels = ['female','male']\n",
    "colors = ['red','yellowgreen']\n",
    "explode = [0.05,0];\n",
    "plt.pie(count_survived_by_sex,explode=explode,labels=labels,colors=colors,\n",
    "                                labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,\n",
    "                                startangle = 90,pctdistance = 0.6)\n",
    "plt.title('Male Female Rate')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.bar(range(len(count_survived_by_sex)), count_survived_by_sex, tick_label=labels,color=colors)\n",
    "plt.title('Male Female Number')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 在所有891人中，男性共577人，女性314人，男女比例为 64.8% 和 35.2%\n",
    "\n",
    "### 2.男女生环数量及比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在所有生还 342 人中 共有109 男性生还 ，233女性生还\n"
     ]
    }
   ],
   "source": [
    "sum_survived_by_sex = new_titanic_df.groupby('Sex').sum()['Survived']\n",
    "female_sum = sum_survived_by_sex['female']\n",
    "male_sum = sum_survived_by_sex['male']\n",
    "total_surived_number = sum_survived_by_sex.sum()\n",
    "print ('在所有生还 %d 人中 共有%d 男性生还 ，%d女性生还' %(total_surived_number,male_sum,female_sum))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fY/xtjPHKGOP2WQNLUiaWVWnFngL8J5AI4ReE8BpCmDTUwo1ac1Gj1vxOo9bc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\nUmX9P1q3R1MOhU/SAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb57e860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "labels = ['female','male']\n",
    "colors = ['red','yellowgreen']\n",
    "explode = [0.05,0];\n",
    "plt.pie(sum_survived_by_sex,explode=explode,labels=labels,colors=colors,\n",
    "                                labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,\n",
    "                                startangle = 90,pctdistance = 0.6)\n",
    "plt.title('Male Female Rate')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.bar(range(len(sum_survived_by_sex)), sum_survived_by_sex, tick_label=labels,color=colors)\n",
    "plt.title('Male Female Number')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 在所有生还342人中，男性共109人，女性233人，男女比例为 31.9% 和 68.1% \n",
    "### 通过对比可以看出，在事故发生后，男性比例出现明显下降\n",
    "\n",
    "### 3.男女生生存率对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "男性生还率为 0.188908 ，女性生还率为 0.742038\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Sex\n",
       "female    0.742038\n",
       "male      0.188908\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_survived_rate = female_sum/female_count\n",
    "male_survived_rate = male_sum/male_count\n",
    "print ('男性生还率为 %f ，女性生还率为 %f' %(male_survived_rate,female_survived_rate))\n",
    "\n",
    "rate_survived_by_sex = new_titanic_df.groupby('Sex').mean()['Survived']\n",
    "rate_survived_by_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x9efff28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#存活率\n",
    "def plotRate(feature,tick_label,color,stacked=False):\n",
    "    # if there are more than one features\n",
    "    if type(feature) is list:        \n",
    "        survived_rate = (new_titanic_df.groupby(feature).mean())['Survived'].unstack()\n",
    "        survived_rate.plot(kind='bar', stacked=stacked)\n",
    "        plt.title('Rate of Survival by {} and {}'.format(feature[0], feature[1]))\n",
    "    # if there is only one feature\n",
    "    else:\n",
    "        survived_rate = (new_titanic_df.groupby(feature).mean())['Survived']\n",
    "        survived_rate.plot(kind='bar',tick_label = tick_label,color = color)\n",
    "        plt.title('Rate of Survival by {}'.format(feature))\n",
    "\n",
    "# call the function to make plots\n",
    "plotRate(\"Sex\",['female','male'],['red','yellowgreen'])\n",
    "#plotRate([\"Pclass\", 'Sex'], False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc68de48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,5))\n",
    "labels = ['female','male']\n",
    "colors = ['red','yellowgreen']\n",
    "plt.subplot(121)\n",
    "plt.bar(range(len(rate_survived_by_sex)), rate_survived_by_sex, tick_label=labels,color=colors)\n",
    "plt.title('Male Female Survived Rate')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 计算得知 男性的生存率为18.9%女性的生存率为74.2%\n",
    "### 女性的生存率明显高于男性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 仓位Pclass\n",
    "\n",
    "### 3种仓位人数和占比情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xUk9poFR8RURkhan4Stlp48h5RFEmdI4QJq6xLuvvuA9RFDFxzXUZPX4V1tt+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1226d2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    216\n",
       "2    184\n",
       "3    491\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_survived_by_class = new_titanic_df.groupby('Pclass').count()['Survived']\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "labels = ['First class','Second class','Third class']\n",
    "colors = ['green','yellowgreen','lightskyblue']\n",
    "explode = [0.05,0,0];\n",
    "plt.pie(count_survived_by_class,explode=explode,labels=labels,colors=colors,\n",
    "                                labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,\n",
    "                                startangle = 90,pctdistance = 0.6)\n",
    "plt.title('Palcss Number rate')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.bar(range(len(count_survived_by_class)), count_survived_by_class, tick_label=labels,color=colors)\n",
    "plt.title('Palcss Number')\n",
    "\n",
    "plt.show()\n",
    "count_survived_by_class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可以看出 在所有乘客中 头等舱有216人，二等舱有 184人，三等舱有 491人 其中各仓位占比为24.2%，20.7% 和 55.1%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3种仓位生存人数占比情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SZk36oV8DZ1prxwIPAOcbY4YDPwRGAfsBVxhjqj3EFRHxToVxCQjiQf8gHvwc+C/hGOKI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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc2f07f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    136\n",
       "2     87\n",
       "3    119\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_survived_by_class = new_titanic_df.groupby('Pclass').sum()['Survived']\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "labels = ['First class','Second class','Third class']\n",
    "colors = ['green','yellowgreen','lightskyblue']\n",
    "explode = [0.05,0,0];\n",
    "plt.pie(sum_survived_by_class,explode=explode,labels=labels,colors=colors,\n",
    "                                labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,\n",
    "                                startangle = 90,pctdistance = 0.6)\n",
    "plt.title('Palcss Survived  Number rate')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.bar(range(len(sum_survived_by_class)), sum_survived_by_class, tick_label=labels,color=colors)\n",
    "plt.title('Palcss Survived Number')\n",
    "\n",
    "plt.show()\n",
    "sum_survived_by_class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可以看出生还人数中  头等舱有136人，二等舱有 87人，三等舱有 119人 其中各仓位占比为39.8%，25.4% 和 34.8%\n",
    "\n",
    "### 3种仓位生存率对比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10974898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Pclass\n",
       "1    0.629630\n",
       "2    0.472826\n",
       "3    0.242363\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "rate_survived_by_class = new_titanic_df.groupby('Pclass').mean()['Survived']\n",
    "labels = ['First class','Second class','Third class']\n",
    "colors = ['green','yellowgreen','lightskyblue']\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "plt.bar(range(len(rate_survived_by_class)), rate_survived_by_class, tick_label=labels,color=colors)\n",
    "plt.title('Palcss Number Survived Rate')\n",
    "plt.show()\n",
    "\n",
    "rate_survived_by_class"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可以看出 头等舱的生还率为 63% 二等舱的生还率为 47.3% 三等舱的生还率为 24.2%\n",
    "### 说明了仓位越好，生还率也越高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 登船港口"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3个登船港口的人数和比例"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\envs\\my_env\\lib\\site-packages\\pandas\\core\\indexing.py:357: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\my_env\\lib\\site-packages\\pandas\\core\\indexing.py:537: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    },
    {
     "data": {
      "image/png": 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mEtt03iFMhSvCVPjPMBV+Gbfk+HHAvUB7v6cVWXubZfIFdUlaez8E0saYR3Ez\n99xgrZ0JPAQ8CtwInOgxn4iIV2qJ7pnJvgP0k2bgx8CPs7nEc7jW6WumTZ75VscOYSpcCPwZ+HOQ\nDsbgRuwfRe08RlJZpuLmVJfVsNZO7fTnbl1cfhZu5VQRkZqmlugiZXOJemCbNe5YfSYCvwTeyOYS\nD2ZziW9nc4kRnXcIU+GcMBVeGKbCBK7P+LnAax6yiqxO3HcAERGpDmqJLt7WwGDfITwKcAMMdwEu\nyuYSd+JaqP81bfLMjvllCVPhi8BPgZ8G6WAnXOv0Ebj5q0V8meg7gIiIVAcV0cVTN4VPDAAOik6L\nsrnETbiC+r5pk2e2dewUpsJHgUeDdHAKsA+uoD4Et8KiSH9SES0iIiWh7hzFUxHdtUbgGGA6MDub\nS/wmm0t8tvMOYSpsDVPh7WEqPAo3fdbRuEUcWvs9rdSqDTP5gqZnFBGRXlurlmhjTALXH3YIrvC+\nH0hba1d0e8USMsYMAo621v6pv25zNdSncs1GA6cAp2RzCQtcA1w9bfLMVzt2CFPhYlyr9dVBOhiJ\n6+pxFG45YZG+NBE3w4SIiEiPrbEl2hjTDPwNOMla+3lgZ2A58Js+zraq0bip13zbyneACmOANPBK\nNpd4NJtLnJTNJVZaJjhMhe+FqfB3YSqcAmwBpICXPGSV2qAuHSIi0mtr0xL9deBP1tqXAKy1oTHm\nHOA1Y8zjwDHW2heNMd8BRltrzzLGfA+3THQI/N1ae5ExZhzwR9zCHMuAbwH1wLXALGBz4Alr7QnG\nmJ2BXwMFYD6uhfIMYIIx5mfARbjCft3oPpwZnd/LWntStGztFGvtwcaYo4GNgC1xxf8mwIbAsdba\nXDEPVjaXWIeVV/GS4kyJTr/J5hJ341qi/zFt8szFHTuEqfAV4Gzg7CAdfBb33B+J+xIlUgoqokVE\npNfWpk/0xqwyTZm1NgTepYvCxhgzAffT/Oej0yHGGANcAFxkrd09Op+JrrIlcDxuAZMDjDGjcYPO\nbsLNUXoFbonpnwPPW2vPxhXNd0erbB0OXA7cDXSsurULMM4Y04BbUe+maPub1tp9gd/hivhibdqD\n68inNeBWQPsb8G42l7g6m0sckM0lVvpSF6bCJ8NU+H3cfNX7AFcBtbK2q/SdLX0HEBGRyrc2RfSb\nwGadNxhj6nCtu+912hxE/07EFd73AvcB6wPjgUnA6dGSsj/jkxbdV6y1i6y1bcBcXEv1L6LL7wUO\nw7VId7Y1bglqrLVvAwuBocBLxpjto/0fxRXVG1lrX4yul4/+nUXPlqrepAfXke6tg/vV4jZgTjaX\nuDibS+zUeYcwFbaFqfDuMBVnNEQSAAAgAElEQVQei1sh8QjgFj79uhBZG/pVQ0REem1tunP8FZhu\njLkFaAGuA2YDt+IK6Q2BF3GzVrwNWOC/wP5R14/vA89G+1xgrf23MWYrPlkJK+ziNo8CrrTW/ijq\nmvEt3Kp4HUX/C7jW5rwxZiyupXoecDNwPvAPXOv5L3At1B26uq1ilOWSwW2tIXf8fjkLW9oJ6mCf\naYNYf+wn34+ef7jAk7cVCOpg0tQY2+0dY8WykJvPX0ZrIWSf4wfStHE9s19s4+2X2tjxoAG+7koT\nbhnhE7O5xGt8MiCx40sQYSpcinsNXhekgxG4XyKOxvXVDz59SJFPGeU7gIiIVL41tkRba2fhipSL\nca2FTbjCeQDwF+ASY8xduP7NWGufxrUgP2yMeRI3UOxt4EdAyhjzQHS9Z7q52f8AV0X77hHt/x4w\nwBjzK1xxvIcx5kFcwfwta20rrrDfCTfN2v24wv6mLo7fU2NKeKySef2pNtrb4KtnD2GnLw7g4euW\nr3T5A1ev4PAzBvPV9GCevG0Fyz4KeeOZNsYn6tnrGwN5dkYrYRiSu3MFif1inu7Fp2yG67bzQjaX\nmJnNJX6QzSU27LxDmAo/CFPhZWEq3AXX1eZ03Bc4ke6sn8kXNEe+iIj0ylp9kFhrZwL7dt5mjNkG\neM1ae00X+5+PaxHu7LVVjxGZ0ul6HeffABJd7Ltdp/OHdHG7C4CBnTYN6HTZsZ3O3wnc2cXx12Rs\nD67T54ZvWEfYHhK2h6xYCnX1KzfINm1Ux/IlIXV1AWEIBDBgEBSWu1NsILzwSCtbbN9Aw4CybMyd\nHJ3Oz+YS9+MGJN44bfLMhR07hKnwTdw0jL8M0sG2uF8zvoLrTy3SWR2uMWCu7yAiIlK5etwaY63t\nriW5WpXlzByxQbCgJeSKHy5h6aKQQ09deVXy9Zvr+NvpS4gNDNhihwYGrROw8cR6Xs218fQ9BT5/\nxAAeuHo5n/vSAKb/aRnrjaxjB39dOrpTB+wZnS7N5hL/whXUd0ybPPPjOcvDVPg08HSQDpK4fvFH\n4frWr9f/kaVMjUJFtIiI9IJ+0izOUN8BujLz9gKbbFPPrl8ZyMJ57Vx37lKO/dUQGgYEtLzZxutP\ntTLtonWIDYLbL16OfawVM6WBPY91jfaP/2MFk/cbwGM3F9jj2IH8+8YVfDC3nREblvWCloNw/aEP\nB+Znc4nrcQX1Q9MmzwwBwlTYDswAZgTp4CTgAFxB/QVW/sVCao/6RYuISK+UdZVUhob4DtCVQesE\nDBwSfHy+vRXa291lA4YENMQCGgZAXV3AkGEByxZ/Mr5y8YJ2Pninneat6imsCKmrc6PzCst6Owaz\nXw3HDT59AHgjm0tksrnEpM47hKlweZgKbw5T4WG4Aup43Owx7f2eVsqBimgREekVtUQXZx3fAbqS\nOCDGnX9YzrVnLaGtFXY5cgCvzmxlxTLYds8Y2+wZ49qzllLfAMNG1jFxt0+e9sduLjDlENd1Y7u9\nY9yQWUrj+gEjN67Y71cbAacBp2VziWdxrdPXTJs8c1bHDmEqXICbf/yKIB2MJRoUKzWl0XcAERGp\nbCqii1OWLdEDBgUcdMrqp73ebm83rV1XOrp0AGy6bQObbltVL4lJuEV9fpnNJR7CFdTXT5s8c37H\nDmEqfNtXOPFKX5xERKRXqqpi6gdlWUTLGgW4AYa7Ar/L5hJ34Arqf02bPHOZ12Tii4poERHpFRXR\nxVERXfkGAAdHp4XZXOImXEF937TJM9U/unaoiBYRkV6p2I6vngxe8y5SQdYFjsWtajk7m0tcGAsf\nHu83kvQTFdEiItIraokuTjv68K14IUF7OxvNbmXCuwUmLWnD1LXRvG5I49EEdd/3nU/6hf4fi4hI\nr6iILs4SYJjvELJ22llnYRtmdoGJ81uZ2NrGZgPbadoABowjCDbCzeQhtUlFtIiI9IqK6OKoiC4z\nrlW5+e2oVXlxG1vVtdHcGNI4hqB+JDDBd0YpS+rKJiIivaIiujhLfQeoVe0MWdSGmdXKxPmtfKbQ\nyuaD2mlaHwaOIwjGAeN8Z5SKssh3ABERqWwqoouzxHeAauZalcfOaWXCu61M+qjVtSoPDVl3DEH9\nKNSqLKXzge8AIiJS2VREF0dFdAmEDP6olS1ntTJpfiufWdHK5gPbGdnRqtwMNPvOKFVv/pp3ERER\nWT0V0cVRd461FELoWpW3fqeVSYtb2Zo2xjWGrDuaoH5DYGvfGaWmqSVaRER6RUV0cT70HaDchAxa\n7FqVJ37QysRCK5vH2hm5AQxqJgjGAmN9ZxTpglqiRUSkV1REF2e27wA+uFblMXNb2XpuKxM/amXr\noI2NhoYM2xDqRhMEW/nOKFIktUSLiEivqIguzizfAfpSyKAlrWwxq5WJ81yr8viY66s8aBxBMAYY\n4zujSImoJVpERHpFRXRx3vIdoBTaGD036qu8qJUJtDFuHdeqXL8hQWB85xPpY4uT8ZjGN4iISK+o\niC5OxbREhwxc2sr4t6K+ysvbGD+gjVHDYfA4gmBDYEPfGUU8ec13ABERqXwqootTdi3RbYx6p5Wt\n5rayzaJWtg5dX+X1RkH9WLUqi3TpVd8BypExph7IAgZoA74BBMCVQAg8B5xorW03xqSAA4FW4BRr\n7RNeQouIeKQiujhzcB8u9f15oyEDlkWtyvOiVuVYG6NHwOBmgmA0MLo/84hUOLVEd+1/AKy1Oxtj\npgIX4oroM621M4wxfwAONsa8CewG7IhbKfRGYHs/kUVE/FERXYRpk2e2ZnOJOfTREtNtjHy3ja3m\nFNjmo1a2bm9j445W5TEEwZZ9cZsiNehl3wHKkbX2H8aYW6M/NwbexbU2PxBtuwPYB7DAdGttCLxl\njGkwxjRZa1v6PbSIiEcqoov3Ar0ookNiy9sY/1aBz7zfyqQVbYxvaGP0cBjSTBCMAkaVLqqIdOEF\n3wHKlbW21RhzFXAocBjwhahYBlgEDAPWBeZ1ulrH9tUW0cOHD6GhoV9/wOsXTU2NviNIpGaei9kF\n3wkqVl+8RlREF+8ZXGtMt9rZ4L1Wtp5bYNKCViaEbWy8Tsjwjr7KWwBb9H1UEenC874DlDNr7THG\nmNOAx4HBnS5qxC04tTA6v+r21Zo/f0mpY3rX1NRIS8si3zGEWnsuBvkOULF6+hrprvhWEV28ZzrO\nuFblzWa1MvH9AhOXtbFFrJ0N1wtdq/JIYKTHnCLyae8n4zF1O+iCMeZrQLO19pfAEqAdeNIYM9Va\nOwPYH7gfeAU4zxhzAdAM1Flr3/cUW0TEGxXRRVrCt3LL+cJ/2hk+EhqaCYLxwHjfuURkreR9Byhj\nNwF/NsY8CMSAU3BdX7LGmAHR+RustW3GmIeAR4E64ERfgUVEfFIRXaSlwXEvAp8BhvjOIiJFe8R3\ngHJlrV0MfLmLi3brYt+zgLP6OJKISFmr8x2g0iTjsTYg5zuHiPSIimgRESkJFdE98x/fAUSkaG3A\nY75DiIhIdVAR3TNqzRKpPM8k47GPfIcQEZHqoCK6Z+7FtWqJSOXQl18RESkZFdE9kIzHPkRdOkQq\njYpoEREpGRXRPXeX7wAiUpSHfQcQEZHqoSK651REi1SOp5Lx2GzfIUREpHqoiO65J1jDUrciUjZu\n9h1ARESqi4roHormi77Xdw4RWSv/8B1ARESqi4ro3rnTdwARWaPXkvHYM75DiIhIdVER3Ts3AwXf\nIUSkW+rKISIiJaciuheS8dg84HbfOUSkW+rKISIiJaciuvf+6juAiKzWe8C/fYcQEZHqoyK6925F\ns3SIlKtrk/FYu+8QIiJSfVRE91IyHlsOXOc7h4h06TLfAUREpDqpiC4NdekQKT8PJeOxF3yHEBGR\n6qQiujQeAV73HUJEVqJWaBER6TMqoksgGY+FwB995xCRj80DbvAdQkREqleD7wBV5DLgDGCo7yBS\nG16e8Q9eecDN3tZWWMEHb77Irif9iuduvZK6+gYGrTuCXU/8BQ0DB398ncKyJTzwu9NY8dECGgYN\nZtcTf8mgdUfw0n038tJ9N7L+pluz0/E/BeCBi05lp2/+jAFDKvIlfWU0XkFERKRPqCW6RJLx2Hzg\nCt85pHZsMfUQ9k9dyf6pK1l/swnseMxPmPn337LnD3/LAWddxbobbsRL99240nVeuu9GNthsAgek\n/8KmO+3PUze5Hg+vPPQvDjz7byz+4D2Wf7SAWbkHGLXV5EotoEG/DImISB9TS3Rp/QY4Eaj3HURq\nx/uvPseHs15hp+POZFxiKoPX2wCAsK2N+tjAlfb9zAFfo729DYDF8+YyeNj6ADQMGERbYTntba0E\ndXW8PONmpp58Qf/ekdK5KxmPveQ7hIiIVDe1RJdQMh57A7hxTfuJlNLT/8iy3WHfBWDI8CYA3nzi\nHuY+/wSb73rQp/avq6vnjnOO4/k7r6E5visA2x76LWZcdCqb7LAnrz58G1tMPZRnb7mCf//pbBbM\nqbgxs2f7DiAiItVPRXTpne87gNSO5YsXsmDO62z4mR0+3vbf2/7Cc7deyT7Jy2gYMLDL6+3/0ys4\n4KyruP/CUwAYtdVk9vrx79hkyn68++JM1h29EUvmtzD5y9/jqRv/0C/3pUTuTcZjWqFQRET6nIro\nEkvGY08CD/rOIbXh3RdmMmbSlI//fvrmy3jnxZnse+afGLTu8E/t/8w/srzy4C0ANAwcTFC3cs+j\nZ/6ZZdJBx9O6fBlBXR0EAYVlS/r2TpRW2ncAERGpDeoT3TfOBab7DiHVb8Hc12kcOQ6ApR++z1M3\n/J71N53A3b/8DgCb7rQfW+1zJHf9fBp7nXYpW0w9lId+fwYv338TYXs7nz/hnI+Ptei9t1mxeBHr\nb7IVYXs7i+fN5e7MCUw+4nte7lsPzEjGYw/5DiEiIrUhCMPQd4aqlMkXpgN7+84hUkN2T8ZjM3yH\nkE9raVlUdR80TU2NtLQs8h1DqK3n4vLZg3xHqFjHNy/r0fWamhqD1V2m7hx951Sg3XcIkRrxoApo\nERHpTyqi+0gyHnsK+JvvHCI14qe+A4iISG1REd23zgR69vuBiKytvyfjMQ3mFRGRfqUiug8l47FZ\nwEW+c4hUscXAj3yHEBGR2qMiuu/9ApjnO4RIlTo3GY+97TuEiIjUHhXRfSwZjy0ATvedQ6QKvQRc\n6DuEiIjUJs0T3T+ywJHA7r6DiFSRk5Px2ArfIaqBMSYGXAFsAgzEzXX/PHAlEALPASdaa9uNMSng\nQKAVOMVa+4SPzCIivqkluh8k47EQmAYs9Z1FpErckozH7vQdooocDcyz1u4C7A9cjGvlPzPaFgAH\nG2MmA7sBO+IaBi7xlFdExDsV0f0kGY+9CvzMdw6RKrAAqJhlFCvE9aw8TWArkAAeiP6+A9gL+Dww\n3VobWmvfAhqMMU39mlREpEyoO0f/+g3wZWB730FEKthJyXjsLd8hqom19iMAY0wjcANues4LrLUd\nKw0uAoYB67LyQOmO7S3dHX/48CE0NNSXOrZ3TU2NviNIpGaei9kF3wkqVl+8RlRE96NkPNaWyReO\nA3JAzHcekQp0XTIe0yJGfcAYMw64GbjUWnuNMea8Thc3Ah8CC6Pzq27v1vz5S0oZtSzU0lLT5a62\nngst+91TPX2NdFd8qztHP0vGY88BP/edQ6QCzQG+4ztENTLGjAKmA6dZa6+INueNMVOj8/sDDwGP\nAPsaY+qMMRsBddba9/s9sIhIGVBLtB/nAnsAu/oOIlIhQuDYZDw233eQKnU6MBz4qTGmo2/0ycBF\nxpgBwAvADdbaNmPMQ8CjuEaYE72kFREpA0EYhmveS0ouky9sCOSBUb6ziFSAi5Lx2Mm+Q0jPtLQs\nqroPmtrqQlDeaum5uHy2unP01PHNy3p0vaamxmB1l6k7hyfJeGwu8FWg3XcWkTL3X+A03yFEREQ6\nUxHtUTIeuw84y3cOkTI2HzgkGY/1rAlBRESkj6iI9u9c4C7fIUTKUBtwZDIee8V3EBERkVWpiPYs\nWs3waGC27ywiZea0ZDw23XcIERGRrqiILgPJeOx94FBgse8sImXir8l47Ne+Q4iIiKyOiugykYzH\nngSOwP2ELVLLngC+5TuEiIhId1REl5FkPHYb8F3fOUQ8mgscqoGEIiJS7lREl5lkPPZHtKKh1KYF\nwAHJeGyO7yAiIiJrohULy1AyHjszky80A8f4ziLST5YBByXjsad8B5HyMfLSdX1HqFjvfXeh7wgi\nVU8t0eVrGnC37xAi/aAVOCIZjz3oO4iIiMjaUhFdppLxWAH4EvCY7ywifagd+HoyHrvFdxAREZFi\nqIguY8l4bBGwLyqkpTqFwDeT8di1voOIiIgUS0V0mUvGYwtxhfTjvrOIlNhJyXjsz75DiIiI9ISK\n6AoQFdL7AI/4ziJSAm3Accl47NKeXNkYkzTG3GOMmW6MucsYkyjy+iOMMV+Nzl9pjNmvJzmKuL2N\njDH/05e3ISIi/U9FdIXo1CJ9r+8sIr2wDPhST1ugjTETgIOAva21+wCnAVcUeZhtomP0lz2Anfvx\n9kREpB8EYRj6ziBFyOQLg4AbgAN9ZxEp0kLcNHYP9PQAxpgNgKeAFHCntfZtY8xAYALwO1wr9zLc\n7DZ1wN+ttVOi6z4GHAlkgW2BM4HPAcM6nU6w1j5hjPkl8FmgEXjBWvsNY8xZwHhgA2AEcClu8O+W\nuOko3wGuxy0Y0wzcAfwM+C8wBDgJmLWanNdGl20OPGGtPaGnj1E5amlZ1KMPGk1x13O1NMVdU1Mj\nLS2LfMfoF5fPHuQ7QsU6vrlna3g1NTUGq7tMLdEVJlrJ7RBcISBSKd4DpvamgAaw1r6Pa0XeGXjU\nGPMi8AXc/4eTrLW74YrbC7s5zM+B+6y1f4z+nmmt3QNX3B5rjFkXmG+t3RtXZE8xxoyN9l1qrd0P\nuAk4wFr7P0AGV5wDbAIcC2yPa4HeNrr8GmvtLd3k3BI4HtgBOMAYM7onj4+IiPQfLbZSgZLxWCvw\nrUy+YIHz0JchKW9vAHsn47FXensgY8x4YKG19rjo788CtwNDrbUdC7U8iCtcV7W61oSZ0b/v4FqM\nlwIjjTHXAh8BQ4FYtE8u+vdD4Pno/Hygo3noaWvtB1G2xwGzym2NWU3OV6y1i6Lrze10PBERKVMq\nvipYMh77NfBFYLHvLCKr8SSwcykK6Mg2wO+NMR1F5ku45cKtMWabaNtu0fZluGK43hizHrBpdHk7\nK7/3rdrVYH9gnLX2K8DpwGA+KcDX1C1ha2PMEGNMPbAjrtDufHtzusi5NscVEZEyo5boCpeMx/6Z\nyRd2Af4FjF3T/iL96CrgO1EXpJKw1t5kjNkaeNwY8xGuOP0x8CZwsTEmwK2AeLy19h1jzN3Af4BX\nohPAq8AkY8wpq7mZJ4CfRn2olwOvAWPWMuIKXL/oUcAN1tqnjTF1wBnGmByuD/RKOYu5/yIiUj40\nsLBKZPKFsbhCOu47i9S8AvCDZDx2se8g/ckYswmdBjLKJzSwsP9pYGF10sDCntPAQlmtZDz2NrAL\ncLXvLFLT3gX2rLUCWkREao+6c1SRZDy2GDg6ky/cC1yMGyQl0l8ex80B/bbvID5Ya98A1AotIlIj\n1BJdhaKFLBLAs76zSM34A7BbrRbQIiJSe1REV6lkPPYibs7ZP/jOIlXtHeDAZDx2QjIeW+47jIiI\nSH9REV3FkvHYsmQ8dgJwOG4aMJFSugGYmIzHbvcdREREpL+piK4ByXjsBtzKaXf6ziJV4UPga8l4\n7PBkPDbPdxgREREfVETXiGQ89mYyHtsfOApo8Z1HKta9wKRkPPY330FERER8UhFdY5Lx2DXAVsCV\nnqNIZfkA+A5u+e7ZvsOIiIj4pinualAyHvsA+EYmX/gbcBmwuedIUr7agSxwhrpuiIiIfEIt0TUs\nGY/dC0wCMrjljUU6exTYPhmPfUcFtIiIyMpURNe4ZDy2NBmP/QQwwN8ArQMv7wLHAjsn47Gc5ywi\nIiJlSd05BHADD4GvZfKFC4HzgL08R5L+txS4BDgnGY8t9B1G+p8xZkfgV9baqcaY8bixEyHwHHCi\ntbbdGJMCDgRagVOstU94Cywi4pFaomUlyXgsn4zH9gb2BZ72nUf6xTLgImCzZDz2YxXQtckYcyrw\nJ2BQtOlC4Exr7S5AABxsjJkM7AbsCByJ+9IlIlKTVERLl5Lx2HRgMvB14FXPcaRvrAAuBcYn47GT\nk/HYO74DiVevAl/s9HcCeCA6fwfu16nPA9OttaG19i2gwRjT1L8xRUTKg4poWa1kPNaejMf+CmwJ\nfBl40nMkKY0CblaW8cl47MRkPPa270Din7X2Rtxro0Ngre0YI7EIGAasy8qrn3ZsFxGpOeoTLWuU\njMfageuB6zP5wh7AqbjuHlJZFuD6uP5fMh57w28UqQDtnc434laqXBidX3V7t4YPH0JDQ31p00m3\nmpoa17xTFamZ+zu7sOZ9pEt98RpRES1FScZj9wH3ZfKFbXDF9BHodVTuXgAuBv6SjMc+8h1GKkbe\nGDPVWjsD2B+4H3gFOM8YcwHQDNRZa99f04Hmz1/Sp0Hl01paFvmO0G+amhpr6P4OWvMu0qWevka6\nK75V/EiPJOOxZ4CjM/nC6cBxwDeAjfymkk7agVuB3yXjsXt8h5GK9EMga4wZgPsidoO1ts0Y8xBu\nDvE64ESfAUVEfArCUNMCS+9l8oU63MCj44BDgIF+E9Wsd3DzfV+ajMde9x1GBKClZVGPPmhGXrpu\nqaPUjPe+WzuT7NRSS/Tls9US3VPHNy/r0fWamhqD1V2mlmgpiajf9HRgeiZfGAEchSuot/MarDZ8\nCNwEXAPcHz0XIiIi0odUREvJJeOxD4DfAb/L5AvbAYcBBwMTvQarLktx3TWuBW5PxmNatl1ERKQf\nqYiWPpWMx54CngLOzOQLm+O6ehwCfA5NsVishcDdwD+BfyTjsdr4/VJERKQMqYiWfpOMx14Ffg38\nOpMvNAH/g2uh3p2Vp82STzwH3AXcBjycjMc0v5GIiEgZUBEtXiTjsRbgCuCKTL7QgFsdbffo9Dlg\nqMd4Pr0OPAjcA9yjVQRFxLd/zJrqO0L3ZvkO0L1Dxs3wHUH6iIpo8S4Zj7UCj0enTCZfqAe2AXaO\nTjsAmwKrHSFbod4H/gM80XFKxmNrnHNXRERE/FMRLWUnGY+1AfnodDFAJl8YCnwGmIQboDgpOjV5\nilmMFcBrwMvAS7jl059IxmOveU0lIiIiPaYiWipCtNJeR2v1xzL5wihccb0JbgW1ZmBcp/Pr9UO8\nVmAe0IL7YfHlVU5vRl8MREREpEqoiJaKlozH3gXeXd3lmXxhHVwxPQrXz7rzaZ1Vzge4grgVKHTx\n73I+KZbfj/5tAT5MxmNatUhERKSGqIiWqpaMxxYDNjqJiIiIlITm6RURERERKZKKaBERERGRIqmI\nFhEREREpkopoEREREZEiqYgWERERESmSimgRERERkSKpiBYRERERKZKKaBERERGRIqmIFhEREREp\nkopoEREREZEiqYgWERERESmSimgRERERkSKpiBYRERERKZKKaBERERGRIqmIFhEREREpkopoERER\nEZEiqYgWERERESmSimgRERERkSKpiBYREZH/397dB1tVVnEc/wJq6ICZiSEKYb6swIAyLULRdMSI\nyWImGy2ZQFR8w9E/SonKtzTzBZtBhRJjSB2lYmwmrEjCzEyRRtMS5eeQyjAWEGZok1zipT/Wc2MH\n95YbL+dwr7/PzBnO2WefZz9nPfty1ln72WebWU1Oos3MzMzManISbWZmZmZWk5NoMzMzM7Oadmt2\nB8zMrHOJiO7ADGAY0AKcLWl5c3tlZtZYrkSbmVldY4Gekj4GTAGmNbk/ZmYN5yTazMzqOhZYACBp\nMXBUc7tjZtZ4TqLNzKyuvYF1lcebIsLTA83sbcX/6ZmZWV2vAb0rj7tL2tjeyn369O62IxvZcsWW\nHXmZdbBz+jzR7C5YMaVPs3vQme3e4S26Em1mZnX9FhgDEBHDgT82tztmZo3nSrSZmdX1Y2BURDwK\ndAPObHJ/zMwartuWLT5cZmZmZmZWh6dzmJmZmZnV5CTazMzMzKwmJ9FmZmZmZjX5xEIzM+uyIuII\n4AZgL6AX8DPgIeBcSafvQHsvAe+XtL7jetn1RMSHgevIuHcHfgVcJWlDA/vQExgn6Y5GbXNXEhFT\ngJOAzcAWYKqkN/17hRGxLzBa0j0RMQeYK2nBTulsbm8AMEzS/J21jY7mSrSZmXVJEbEPMBe4RNIJ\nwHBgCBBN7VgXFxEHAXcDkyUdCxwDtADfbnBX+gJnN3ibu4SIGAx8Ghgl6WTgMmB2zWaGljYa5URy\nX+k0/OscZmbWJUXEeOBISRdXlvUCRpBV0jXA/sB8SVdGxBBgOvmzfa8AE4EPAdcDG4DbgW8AvwYG\nAquB8cBGMkE5BOgB3CzpBxHxEHCepGURcR6Z1M0B5pf2W6vitwGvl/6slzRhZ8SjUSJiKtAiaVpl\nWTfgBfI9jq/GpMT+IuALZMV0rqTpEdGfjHlPYD0wiYzvvcBKMt5LJJ0fEccA04B/Aa8CZwA3A6cB\nN5Hjejd5tc3dgK+V+ydJmhwRXwGGS/pMRIwDBgCHk8n/QOAAYIKkJ3dK0DpYROwHPAVcASyQ9HJE\nvAMYDNwCbCJjeg5ZUJ0raXh57WLgdGAWMIyM1QjgnZXb+ZKWRMR1wFHkxZeek3RmRFwJHArsB+wL\nzAA+S8ZzPLAK+BHwF+Ag4OfA5cBS8sjFZHJ82+rndmPfwaGrxZVoMzPrqvqRidt/SPoHmRD3BMYC\nI8kPbcik4UJJHycT3EvL8p6SRkq6qzyeKel44CXyw/1cYK2kEeTh82tKEtOevsDJkm4AvkMmZycC\nf3oL73VX8l62j/sW8ktH321XLlXT04Bjy21sRAQl+S1HEW4CvlVecjhwFvARYExE9CXH8j7gePIL\nzbuAa4FnJV1NJoILJaJytsMAAAPcSURBVB0HfA74HrAQOK60ORLoXy5ff0ppC2CFpE+QCd2ktxCT\nhpK0lqwiHwM8FhHLgE+R+/jksv/OIL9otOda4EFJt5fHT5T99BZgQkTsDbwqaRSZZA+PiAPLum9I\nGk3GcYykU8jxa51CNRCYABxNVqCHlefvkfST/9HPtsa+aZxEm5lZV7UC6F9dEBEHk4nTM5JaJP2T\nrCQDDAJmlAryRDIJB1CliQ2SFpf7j5JTQwYBDwNIeh14lqyUVVUvff5iZW5wP0lLy/3f1H6Hu6YV\nwPuqCyKiO1ndXVNZ3BqTD5CJ9yLgQeDdZCVzCDC1jMfl5FEDgOWSXpe0iaxm9gS+WZ5fBJxKVqSr\nqmP0Mnnp+l7A8xFxdFn/MXLfGCBpWXnd78u/K8t2OoWIOBR4TdJESQOAccBMYLCkp8pqDwNHtPHy\nbm0sA2idT72KrBi/AewfEfcC3yXj2Xpt7daK/d/JvwfIIwStMXxa0t/KGD7O9lOs+rXTz7bGvmmc\nRJuZWVd1PzA6Ig4BiIjdyYrWWnLawLYEfLFUoi8FflqWb66ss0dEfLDcHwk8AzxX7hMRvcnk70Xy\nMPQBZd0jK21U21tZKrGQc7a7gruAsyPisIjYJyIeAO4gx+MVto+JyEP5J5TYzyEvJb8MuKwsOxeY\nV9Zva+zOAOaUqvVSsmq8ma15TnWMDiQr1a+QV9+8kTzx8RdkMv7LSruddc7rUGBmObkS4HlgHaCI\nGFqWHV+WryeT4R7lPIKDy/PV+MH2sfgk0F/S54GpwJ5sTcD/X9wGRcReEdED+CiZaFe39+c2+vlm\n2m0o/zqHmZl1SZJeK/OiZ5VKaG9yPvJzbD2MX3U+cGf5YIc8bNxvm3VagIsi4jCy4jqFTBxmRcQj\nZCJxlaQ1ETEduC0iVgIvt9PNC4DZEdE6zaS99ToNSSvLvOJbyerkXuTc1tXAnWwTE0lPR8Qi4JEy\nb3dJee5LbE0E9wQu3m5jW/0O+H4ljpPIqvceEXE9mRzPjohTS1uTJG2MiPvJ6R8XkNXmeeR+0KlJ\nui8iBgGPl5h0B75M7rO3ljnqG4GzJK2KiIVkDJeXG+T0oiERcUk7m1kCfL3MoW4hp/Bs+/fSng3k\nvOj3APPKPtAd+GpEPElOk/qvftZ5/43iEwvNzMyaJCIuBH4o6a8RcQ05XeTqZvdrZyiVxRfKvHR7\nm4qIgVROZOzMXIk2MzNrntXAA6VauI789YIuSdIfmt0Hs47kSrSZmZmZWU0+sdDMzMzMrCYn0WZm\nZmZmNTmJNjMzMzOryUm0mZmZmVlNTqLNzMzMzGpyEm1mZmZmVtO/AasAiBUX2qa1AAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc256fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "NewEmbarked\n",
       "C    169\n",
       "Q     78\n",
       "S    644\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#因为在登船港口中存在缺失数据，所有我们通过用前一个有值得数据港口来填充\n",
    "new_titanic_df.loc[:,'NewEmbarked'] = new_titanic_df['Embarked'].fillna(method='pad')\n",
    "\n",
    "count_survived_by_embarked  =  new_titanic_df.groupby('NewEmbarked').count()['Survived']\n",
    "\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "labels = ['Cherbourg','Queenstown','Southampton']\n",
    "colors = ['green','yellowgreen','lightskyblue']\n",
    "explode = [0.05,0,0];\n",
    "plt.pie(count_survived_by_embarked,explode=explode,labels=labels,colors=colors,\n",
    "                                labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,\n",
    "                                startangle = 90,pctdistance = 0.6)\n",
    "plt.title('Embarked  Number rate')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.bar(range(len(count_survived_by_embarked)), count_survived_by_embarked, tick_label=labels,color=colors)\n",
    "plt.title('Embarked  Number')\n",
    "\n",
    "plt.show()\n",
    "count_survived_by_embarked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 从上面可以看到 从Cherbourg登陆的人有169人，在Queenstown登陆的有78人，在Southampton登陆的有644人\n",
    "### 3个港口占比分别为 19% ，8.8%，72.3%\n",
    "\n",
    "## 3个港口登陆的生还人数和占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 406,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eT15Rjt4GDk0mMi/lWpjOhjHcCPIpBU0lxa5o/n+LiEjxq/RS/Hohn6wAJ68o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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc238470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "NewEmbarked\n",
       "C     94\n",
       "Q     31\n",
       "S    217\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum_survived_by_embarked  =  new_titanic_df.groupby('NewEmbarked').sum()['Survived']\n",
    "\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "labels = ['Cherbourg','Queenstown','Southampton']\n",
    "colors = ['green','yellowgreen','lightskyblue']\n",
    "explode = [0,0,0.05];\n",
    "plt.pie(sum_survived_by_embarked,explode=explode,labels=labels,colors=colors,\n",
    "                                labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,\n",
    "                                startangle = 90,pctdistance = 0.6)\n",
    "plt.title('Embarked  Number rate')\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.bar(range(len(sum_survived_by_embarked)), sum_survived_by_embarked, tick_label=labels,color=colors)\n",
    "plt.title('Embarked  Number')\n",
    "\n",
    "plt.show()\n",
    "sum_survived_by_embarked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 从上面可以看到 从Cherbourg登陆的人中有94人生还，在Queenstown登陆的人中有31人生还，在Southampton登陆的人中有217人生还  占比分别为 27.5% ，9.1%，63.5%\n",
    "\n",
    "## 从3个港口登陆的生还率分别是多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc44a4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Embarked\n",
       "C    0.553571\n",
       "Q    0.389610\n",
       "S    0.336957\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rate_survived_by_embarked  =  new_titanic_df.groupby('Embarked').mean()['Survived']\n",
    "\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "labels = ['Cherbourg','Queenstown','Southampton']\n",
    "colors = ['green','yellowgreen','lightskyblue']\n",
    "plt.bar(range(len(rate_survived_by_embarked)), rate_survived_by_embarked, tick_label=labels,color=colors)\n",
    "plt.title('Embarked Survived  rate')\n",
    "plt.show()\n",
    "\n",
    "rate_survived_by_embarked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 从上面可以看到 从Cherbourg登陆生还率为55.6%，在Queenstown登陆生还率为39.7%，在Southampton登陆生还率为33.7%\n",
    "### 由以上数据可以看出在Cherbourg登陆的乘客生还率明显高于其他2个港口"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对有兄弟姐妹的生存率计算 \n",
    "\n",
    "### 如果有兄弟姐妹，也就是SibSp不为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SibSp\n",
       "0    608\n",
       "1    209\n",
       "2     28\n",
       "3     16\n",
       "4     18\n",
       "5      5\n",
       "8      7\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_survived_by_sibsp  =  new_titanic_df.groupby('SibSp').count()['Survived']\n",
    "count_survived_by_sibsp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 从上面可以看出来 没有兄弟姐妹的有608人  有1个的为209人 有2个的为28 有3个的16 有4个的为18人 有5个的 5人 有8个的7人"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LyV6T/f3It0a+obX+Drss55UKLl72yCMMufZahl5xBY7XN+a5ej+D1Bj2HDt5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\nERGRhqViVkREREQa1v8HXfZvY56aSaoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc029ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "SibSp\n",
       "0    608\n",
       "1    209\n",
       "2     28\n",
       "3     16\n",
       "4     18\n",
       "5      5\n",
       "8      7\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 409,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_survived_by_sibsp  =  new_titanic_df.groupby('SibSp').count()['Survived']\n",
    "\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "labels = ['No SibSp','One SibSp','Two SibSp','Three SibSp','Four SibSp','Five SibSp','Eight SibSp']\n",
    "colors = ['green','yellowgreen','lightskyblue','red','lightcyan','blueviolet','aqua']\n",
    "explode = [0.05,0,0,0,0,0,0];\n",
    "plt.pie(count_survived_by_sibsp,explode=explode,labels=labels,colors=colors,\n",
    "                                labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,\n",
    "                                startangle = 90,pctdistance = 0.6)\n",
    "plt.title('SibSp  Number rate')\n",
    "#plt.legend()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.bar(range(len(count_survived_by_sibsp)), count_survived_by_sibsp, tick_label=labels,color=colors)\n",
    "plt.title('SibSp  Number')\n",
    "\n",
    "plt.show()\n",
    "count_survived_by_sibsp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 从上面可以看到 没有兄弟姐妹的占比68.2,有兄弟姐妹的其中有1个的占比23.5%，有2个的占比3.1%，有3个的占比1.8%，有4个的占比2%，有5个的占比0.6%，有2个的占比0.79%\n",
    "\n",
    "## 由于超过2个兄弟姐妹的占比比较低，所有统计的时候重新划分为1个以上的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\envs\\my_env\\lib\\site-packages\\pandas\\core\\indexing.py:357: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\my_env\\lib\\site-packages\\pandas\\core\\indexing.py:537: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "#首先对数据先进行处理\n",
    "def deal_sibsp_data(item):\n",
    "    if(item == 0):\n",
    "        return 'No SibSp'\n",
    "    elif(item == 1):\n",
    "        return 'One SibSp'\n",
    "    else:\n",
    "         return 'One More SibSp'\n",
    "\n",
    "new_titanic_df.loc[:,'NewSibSp'] = new_titanic_df['SibSp'].apply(deal_sibsp_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Pq6uYMTjDhyOWt54W1XpaOlTMiojIGklDMatfdpXJ44vvGpYsjFtP2/uezjRV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\nERERjZVgNiIiIiIa6x/XFvb41nWAbwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc707e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "NewSibSp\n",
       "No SibSp          608\n",
       "One More SibSp     74\n",
       "One SibSp         209\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 411,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_survived_by_sibsp  =  new_titanic_df.groupby('NewSibSp').count()['Survived']\n",
    "\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "labels = ['No SibSp','One More SibSp','One SibSp']\n",
    "colors = ['green','yellowgreen','lightskyblue']\n",
    "explode = [0.05,0,0];\n",
    "plt.pie(count_survived_by_sibsp,explode=explode,labels=labels,colors=colors,\n",
    "                                labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,\n",
    "                                startangle = 90,pctdistance = 0.6)\n",
    "plt.title('SibSp  Number rate')\n",
    "#plt.legend()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.bar(range(len(count_survived_by_sibsp)), count_survived_by_sibsp, tick_label=labels,color=colors)\n",
    "plt.title('SibSp  Number')\n",
    "\n",
    "plt.show()\n",
    "count_survived_by_sibsp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 从上面可以看到 没有兄弟姐妹的占比68.2,有兄弟姐妹的其中有1个的占比23.5%，1一个以上的占比8.3%\n",
    "\n",
    "## 这3中情况的生存率分别是多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hKkkFDFNJKmCYSlIBw1SSChimklTAMJWkAoapJBUwTCWpgGEqSQUMU0kqYJhKUgHDVJIK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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb8ff278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Embarked\n",
       "C    0.553571\n",
       "Q    0.389610\n",
       "S    0.336957\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 412,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rate_survived_by_sibsp  =  new_titanic_df.groupby('NewSibSp').mean()['Survived']\n",
    "\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "labels = ['No SibSp','One More SibSp','One SibSp']\n",
    "colors = ['green','yellowgreen','lightskyblue']\n",
    "plt.bar(range(len(rate_survived_by_sibsp)), rate_survived_by_sibsp, tick_label=labels,color=colors)\n",
    "plt.title('SibSp Survived Rate')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "rate_survived_by_embarked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 由上面可以看出 没有兄弟姐妹的生还率占34.5% 明显低于有兄弟姐妹的，在有兄弟姐妹的人中有一个兄弟姐妹的占比最多，但是由于有一个以上的兄弟姐妹人多太少 ，所以有兄弟姐妹的生还率跟兄弟姐妹的人数并不能直接得出结论"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parch 父母子女\n",
    "\n",
    "### 有父母子女的人数和占比"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\ProgramData\\Anaconda3\\envs\\my_env\\lib\\site-packages\\pandas\\core\\indexing.py:357: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "D:\\ProgramData\\Anaconda3\\envs\\my_env\\lib\\site-packages\\pandas\\core\\indexing.py:537: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "#首先对数据先进行处理\n",
    "def deal_parch_data(item):\n",
    "    if(item == 0):\n",
    "        return 'No Parch'\n",
    "    else:\n",
    "         return 'Parch'\n",
    "        \n",
    "new_titanic_df.loc[:,'NewParch'] = new_titanic_df['Parch'].apply(deal_parch_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mrJbuxLIqNao7UgdoJDHGt4cQTgF+A0zu860O4AlgYXl9ze0Dmj9/8UjHVJOZ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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc449da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "NewParch\n",
       "No Parch    678\n",
       "Parch       213\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 414,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_survived_by_parch =  new_titanic_df.groupby('NewParch').count()['Survived']\n",
    "\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "labels = ['No Parch','Parch']\n",
    "colors = ['green','yellowgreen']\n",
    "explode = [0.05,0];\n",
    "plt.pie(count_survived_by_parch,explode=explode,labels=labels,colors=colors,\n",
    "                                labeldistance = 1.1,autopct = '%3.1f%%',shadow = False,\n",
    "                                startangle = 90,pctdistance = 0.6)\n",
    "plt.title('Parch  Number rate')\n",
    "#plt.legend()\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.bar(range(len(count_survived_by_parch)), count_survived_by_parch, tick_label=labels,color=colors)\n",
    "plt.title('Parch  Number')\n",
    "\n",
    "plt.show()\n",
    "count_survived_by_parch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 以上可以看出 没有父母子女在人有678人 有父母子女的有213人 占比分别为 76.1% 和 23.9%\n",
    "\n",
    "## 他们的生存率分别是多少"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb6d4518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "NewParch\n",
       "No Parch    0.343658\n",
       "Parch       0.511737\n",
       "Name: Survived, dtype: float64"
      ]
     },
     "execution_count": 415,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rate_survived_by_parch  =  new_titanic_df.groupby('NewParch').mean()['Survived']\n",
    "\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(12,5))\n",
    "plt.subplot(121)\n",
    "labels = ['No Parch','Parch']\n",
    "colors = ['green','yellowgreen']\n",
    "plt.bar(range(len(rate_survived_by_parch)), rate_survived_by_parch, tick_label=labels,color=colors)\n",
    "plt.title('Parch Survived Rate')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "rate_survived_by_parch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 由以上可以看出 没有父母子女的生还率为34.4% 有父母子女的占比为51.2%"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 总结\n",
    "\n",
    "## 数据分析总结"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "本项目主要分析生存各种情况下乘客的生存率，下面详细叙述\n",
    "\n",
    "1.首先数据中一共有891人，其中有342人生还，有549人死亡， 生还率为38.4%。\n",
    "\n",
    "2.在891人中，因为年龄存在缺失，所以通过去掉不存在年龄的数据进行了计算。 在最后进行计算的数据中一共有714人，其中最大年龄为80，最小年龄为\n",
    "0.42，平均年龄29.7 标准差为14.5。通过计算最终按4个年龄段进行了划分：1.（0-14岁）儿童的生存率为58.4% 2.(15-30岁)青少年的生存率36.1% 3.（30-60岁）中年人的生存率 41.2% 4.(59-100岁)老年人生存率为27.0%。由于在数据中对年龄的分段存在随意性，老年人的数据也较少，所以不好得出结论\n",
    "\n",
    "3.在891人中，共有男性577人 ，女性314人，男女比例为64.8%和35.2%，男性的生存率为18.9%，女性的生存率为74.2%。可以得出女性的生存率明显高于男性，也侧面说明了，在灾难中的救援准则 妇孺优先。\n",
    "\n",
    "4.根据这组数据，得知泰坦尼克号上的船舱分为3个等级 1.头等舱，2.二等舱 3.三等舱。在所有891人中，头等舱有136人，二等舱有87人，三等舱有119人 其中各仓位人数占比为39.8%，25.4% 和 34.8%，头等舱的生还率为63%， 二等舱的生还率为47.3% ，三等舱的生还率为24.2%。可以看出头等舱的生还率明显高于三等舱，所以出门在外，在条件允许的情况下，还是要坐更好的船舱。\n",
    "\n",
    "5.根据这组数据，得知泰坦尼克号上的乘客登船港口一共有3个，分别为Cherbourg港口，Queenstown港口，Southampton港口。由于其中有2个数据缺失登船港口，所以通过和缺失数据的上一个对应的港口进行填充。其中从Cherbourg港口登船的有169人生还94人，在Queenstown港口登船的人有78人生还31人，在Southampton港口登船的有644人生还217人。生还人数占比分别为27.5% ，9.1%，63.5%。\n",
    "从Cherbourg登船的生还率为55.6%，在Queenstown登船的生还率为39.7%，在Southampton登船的生还率为33.7%。可以看出从Cherbourg港口登船的生还率最高\n",
    "\n",
    "6.在891人中，没有兄弟姐妹的有608人，有1个的为209人，有1个以上的为74人。没有兄弟姐妹的占比68.2%,有1个的占比23.5%，一个以上的占比8.3%。没有兄弟姐妹的生还率仅为34.5%，明显低于有兄弟姐妹的人。所以也可以看出，也许在灾难中家人的鼓励也是生存下去的动力。\n",
    "\n",
    "7.在891人中，没有父母子女在人有678人，有父母子女的有213人 占比分别为76.1%和23.9%。没有父母子女的生还率为34.4% 有父母子女的占比为51.2%，明显高于没有父母子女的人。\n",
    "\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```\n",
    "由于数据总体只有891人，明显少于实际的数据，所以以上得出的结论都只是暂时的，并不能代表实际情况，还有在处理数据过程中，有一定的随意性，所以得出的结论也不具有代表性。\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
